ML RCUT1: Difference between revisions

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{{DISPLAYTITLE:ML_RCUT1}}
{{DISPLAYTITLE:ML_RCUT1}}
{{TAGDEF|ML_RCUT1|[real]|5.0}}
{{TAGDEF|ML_RCUT1|[real]|8.0}}


Description: This flag sets the cutoff radius <math>R_\text{cut}</math> for the radial descriptor <math>\rho^{(2)}_i(r)</math> in the machine learning force field method.
Description: This flag sets the cutoff radius <math>R_\text{cut}</math> for the radial descriptor <math>\rho^{(2)}_i(r)</math> in the machine learning force field method.

Revision as of 11:55, 31 March 2023

ML_RCUT1 = [real]
Default: ML_RCUT1 = 8.0 

Description: This flag sets the cutoff radius for the radial descriptor in the machine learning force field method.


The radial descriptor is constructed from

and is an approximation of the delta function. A basis set expansion of yields the expansion coefficients which are used in practice to describe the atomic environment (see this section for details). The tag ML_RCUT1 sets the cutoff radius at which the cutoff function decays to zero.

Mind: The cutoff radius determines how many neighbor atoms are taken into account to describe each central atom's environment. Hence, important features may be missed if the cutoff radius is set to a too small value. On the other hand, a large cutoff radius increases the computational cost of the descriptor as the cutoff sphere contains more neighbor atoms. A good compromise is always system-dependent, therefore different values should be tested to achieve satisfying accuracy and speed.

The unit of the cut-off radius is . The value of ML_RCUT1 is the default value for ML_RCUT2.

Related tags and articles

ML_LMLFF, ML_RCUT2, ML_W1, ML_SION1, ML_SION2, ML_MRB1, ML_MRB2

Examples that use this tag